Radar backscatter from an ocean surface is commonly referred to as sea clutter. Any radar backscatter not due to the scattering from an ocean surface can be considered a potential target. The amplitude statistics of clutter have been modeled by Rayleigh, log-normal, contaminated-normal, Weibull, log-Weibull and K-distributions. Maritime surveillance radar relies on non-coherent processing techniques and the amplitude statistics of the sea clutter backscatter determines radar sensitivity and false alarm performance.
In one type of surveillance radar system designed to detect low radar cross section targets such as periscopes or small watercraft against a background of sea clutter, high range resolution is applied to minimize sea clutter competing with the target. The scan rate is chosen to be sufficiently high to provide multiple observations while the target is present. The system sensitivity is set to provide a high probability of detecting small targets of interest while accepting a high likelihood of also detecting tens of thousands of false plot detections per radar scan from noise and sea clutter. The false plot detections are filtered out over the observation time using integration along-a-path techniques such as retrospective track-before-detect processing that are capable of operating in a high false activity rate environment without incurring miss-association errors that are typically encountered in Track-While-Scan systems. The retrospective track-before-detect process maintains a history of scan level detections over the observation time and integrates along prior velocity trajectories searching for persistent returns that are indicative of a real target. The plots from sea clutter and noise will have little correlation in their position from scan to scan, being “noise like” and will have a low probability of integrating up and passing the retrospective process. The end result is a track picture that is sufficiently clean of false and clutter tracks that it can be readily understood.
A key factor that determines the detection performance is the ability to provide a first threshold on each range/azimuth cell that maintains a near Constant False Alarm Rate to fit within the radars processing resources in the presence of an unknown statistical background. There are numerous Constant False Alarm Rate techniques in the literature that develop thresholds based on a measure of the background region around the cell under test using both parametric or non-parametric algorithms. The parametric CFAR requires knowledge of the underlying statistics while the non-parametric CFAR is distribution free. CFAR techniques are based on an interference background (noise and sea clutter) that is assumed to be spatially uncorrelated. Large background regions are typically used to minimize the CFAR loss (which is the loss between the ideal threshold and the one computed by the CFAR). A guard band is typically employed that separates the cell under test from the background region and prevents extended targets from entering the background region and affecting sensitivity. CFAR designed for spatially uncorrelated backgrounds will introduce a higher threshold in the upwind direction to adapt to the higher clutter levels returned.
Recent studies of sea clutter phenomenology by Watts and Ward suggest that at certain aspect angles relative to the wind the sea clutter is spatially correlated (upwind and downwind). CFAR techniques have been developed to take advantage of this property and they result in a significant CFAR gain (>5 dB) when compared to standard techniques that have a large background region. These CFAR gain techniques require that the background estimate be derived from a region very close to the cell under test and also that the background size be on the order of the spatial correlation interval. Since spatial correlations are on the order of 10 meters, the number of background cells is much less than what is typically used in traditional CFAR designed for low CFAR loss. Therefore, CFAR designed for CFAR gain in correlated backgrounds will have high CFAR loss in uncorrelated backgrounds such as crosswind and noise limited conditions. Another problem is that CFAR gain techniques have problems with extended targets since the background measurement is taken with virtually no guard band.
To mitigate this problem, the current method is to build a clutter map where the statistics of each region are measured over time. The appropriate CFAR parameters are applied in each region. The problem with this approach is that it takes time to develop the clutter map since sufficient averaging is required to accurately characterize the clutter backscatter; the CFAR loss is higher because clutter variations within a clutter map cell are not tolerated, there are discontinuities at the clutter map cell boundaries and the clutter map solution still does not address the detection of extended targets in regions where CFAR gain techniques are applied.